The dynamics of geographic versus sectoral diversification: a causal explanation. Working paper
Bibliographic record
Abstract
Emerging Markets and the Global Economy for helpful comments, as well as Ravi Jagannathan and Rene ’ Stulz for useful discussions on the topic. Jean-Martin Payeur has provided valuable research assistance. The authors are grateful for financial support from IFM2, SSHRC, and BSI GAMMA Foundation. Errunza also acknowledges financial support from the Bank of Montreal Chair at McGill University. The Dynamics of Geographic versus Sectoral Diversification: Is There a Link to the Real Economy? We study the dynamics of gains from sectoral versus geographic diversification and relate economic sources to changes in those gains. We estimate conditional correlations between returns on the U.S. equity market and 16 equity markets and 10 local industries from other OECD countries and find that the average correlation across countries has increased in relation to that across industries. We also show that this process is accompanied by increased alignment in the industrial structures across countries and an increase in the average conditional correlation of aggregate production growth across countries relative to that of disaggregated production growth, especially among developed economies. Thus, the increased benefits of industry-level investing across developed markets are reflected in the real side of
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".